Hierarchy embedded differential image for progressive transmission using lossless compression

Algorithms for constructing differential images with hierarchical data structure are presented. The data structures are simple, efficient, and ideal for viewing images in progressive transmission using lossless compression. Unlike conventional pyramidal structures, the total number of nodes required to build the structure is the same as the number of pixels in an image at the same time its hierarchy is preserved. These structures are constructed using subsampling or mean-sampling methods for predictors with block sizes of 2×2 or 3×3. Experiments were conducted to compare these structures in terms of their first order entropy and RMS errors in the reconstruction process. Results indicate that the mean-sampling with circular-difference method yields the lowest entropy, comparable to that with 1-D lossless DPCM predictive coding. Lastly, hardware for the efficient construction and access of the hierarchical structures is discussed and evaluated